Cited by 104Survey Methodology and NonresponseSurvey Sampling and Estimation TechniquesAdvanced Causal Inference Techniques

The DOI matches its Crossref record.

Population-Based Survey Experiments

Mutz, Diana C.·2011·Princeton University Press eBooks

Abstract

Population-based survey experiments have become an invaluable tool for social scientists struggling to generalize laboratory-based results, and for survey researchers besieged by uncertainties about causality. Thanks to technological advances in recent years, experiments can now be administered to random samples of the population to which a theory applies. Yet until now, there was no self-contained resource for social scientists seeking a concise and accessible overview of this methodology, its strengths and weaknesses, and the unique challenges it poses for implementation and analysis. Drawing on examples from across the social sciences, this book covers everything you need to know to plan, implement, and analyze the results of population-based survey experiments. But it is more than just a “how to” manual. This book challenges conventional wisdom about internal and external validity, showing why strong causal claims need not come at the expense of external validity, and how it is now possible to execute experiments remotely using large-scale population samples. Designed for social scientists across the disciplines, the book provides the first complete introduction to this methodology and features a wealth of examples and practical advice.

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Crossref · 10.23943/princeton/9780691144511.001.0001

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Metadata

Authors
Mutz, D. C.
Corresponding author
Mutz, Diana C.
Venue
Princeton University Press eBooks
Published
2011-07-25
OpenAlex
W4230818916
Cited by
104
References
0
Citations by year
2026: 7 · 2025: 9 · 2024: 25 · 2023: 11 · 2022: 5 · 2021: 4 · 2020: 9 · 2019: 8 · 2018: 6 · 2017: 7 · 2016: 5 · 2015: 2 · 2014: 5 · 2012: 1
FWCI
2.11
Type
book
Language
en
Open access
closed
Locations
  • Princeton University Press eBooks (publishedVersion)
  • Project Muse (Johns Hopkins University) (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
  • Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
randomized experiments, causal inference, generalizability, internal validity, remote experimentation, experimental design

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